Before hiring an AI consulting firm, make it prove six things: that AI addresses a defined business problem, that the named team can deliver it, that your data is usable and protected, that the proposed system can be tested under real conditions, that the contract gives you meaningful control, and that your organization can operate the result after the engagement. Use the same questions with every finalist, ask for evidence rather than demonstrations, and record whether each answer is backed by a reference, policy, contract term, inspected configuration, or observed test.
The guidance below is based mainly on official US and UK procurement and security guidance. Requirements can differ by jurisdiction, industry, and risk level, so involve your legal, privacy, security, and procurement specialists before signing.
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1. Do we have a problem that calls for AI?
Start with the outcome, not a model name. A credible firm should be able to describe the business problem, the people affected, the constraints, and the measurable result that would make the project worthwhile.
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Questions to ask
- What specific business problem are you solving, and for whom?
- What baseline will we measure against, and what target would count as success?
- Why is AI appropriate for this problem?
- What simpler software, process, rules-based, or non-AI approaches did you consider?
- What assumptions about workflow, volume, accuracy, latency, or human review are built into your proposal?
Ask for an output-based proposal that states user needs, constraints, performance requirements, dependencies, and opportunities to iterate. UK government AI procurement guidance recommends clearly stating the challenge, explaining why AI is relevant, and remaining open to alternatives. NIST procurement guidance likewise emphasizes defining the problem and assessing risk before prescribing a solution; see the NIST guidelines for AI procurement.
Do not accept a generic promise such as “automate your business with generative AI.” Require a measurable use case, such as reducing average document-review time while maintaining a specified error rate, and identify who owns that measurement. A proposal that cannot state what will be different for a real user is not ready for contracting.
2. Who will actually deliver the work?
The people in a sales presentation are not necessarily the people who will build, integrate, secure, and support your system. Get the delivery team and its availability in writing.
Questions to ask about the team
- Who is the engagement lead, technical lead, security lead, domain specialist, and delivery manager?
- How much time will each named person commit, and for which milestones?
- Which work will be performed by subcontractors, cloud providers, model providers, or other outside services?
- What projects resemble ours in data, users, regulatory exposure, scale, and failure consequences?
- May we speak with comparable clients, including one whose project missed its original target?
- What went wrong in that project, and what changed in your methods afterward?
Evaluate capabilities against the work: data engineering, software integration, machine-learning evaluation, security, privacy, change management, and the relevant business domain may all be necessary. The official guidance supports multidisciplinary evaluation but does not establish a universal credential requirement or a minimum number of references. Treat a polished résumé or a large client list as a starting point, not proof of fit.
Verify capacity and accountability
Ask who has authority to make technical and scope decisions, how issues are escalated, and what happens if a key person leaves. The contract should identify the party responsible for subcontractors and require advance notice of material substitutions. Request a delivery plan with milestones, dependencies, review gates, and named acceptance owners on both sides.
3. What does the firm need from our data?
AI feasibility depends on data that is available, lawful to use, sufficiently complete, and fit for the intended task. Have the firm perform discovery before it treats implementation as feasible.
Data-readiness questions
- What datasets, documents, logs, labels, metadata, and interfaces do you need?
- How will you assess quality, completeness, representativeness, provenance, bias, and drift?
- Who owns or licenses each source, and are there restrictions on training, inference, retention, or onward transfer?
- What minimum volume and quality are required, and what happens if our data does not meet those assumptions?
- Which data will be copied, transformed, or combined with other customers’ data?
Define the data boundary before sharing anything. Specify what will be shared, the permitted purpose, processing and storage locations, access roles, subcontractor or model-provider access, retention period, and deletion or return method. Require a documented process for correcting, exporting, and securely disposing of data.
Security questions
- How is sensitive information encrypted in transit and at rest?
- How are identities, privileged access, keys, environments, and administrative actions controlled?
- Are customer prompts, files, or outputs used to train a shared model?
- How are employees trained, and how is access removed when staff or subcontractors leave?
- What security assessments, audit reports, penetration tests, or configuration evidence can we inspect?
- How quickly will you notify us of a suspected or confirmed incident, and what information will the notice include?
The Federal Trade Commission advises asking service providers how they secure data, who can access it, and how staff are trained. Its guidance states: “Before bringing service providers on board, spell out what you expect in terms of security.” Put those expectations, performance standards, monitoring methods, and remedies into the contract, then verify compliance rather than relying on assurances. See the FTC’s service-provider security guidance.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstall4. What exactly is the proposed AI system, and how will you test it?
“AI-powered” is not a technical specification. Require an understandable description of the models, algorithms, data sources, integrations, and third-party services that will produce an output.
System and model questions
- Which model or models, versions, algorithms, retrieval sources, tools, and orchestration components are proposed?
- What training data or other data informed the approach, and what rights cover that data?
- What known limitations, exclusion cases, hallucination modes, bias risks, or security weaknesses apply?
- Under what conditions could quality, speed, cost, or availability deteriorate?
- What information can a user see about how an output was produced?
- Where is human review mandatory, and how are uncertain or harmful outputs escalated?
Ask for a test plan based on your actual users and failure risks, not only a favorable demonstration. It should define representative test data, success and failure metrics, edge cases, unacceptable outcomes, reproducible procedures, and who signs acceptance. Include tests for privacy leakage, unauthorized actions, prompt or input manipulation, bias across relevant groups, and graceful handling of missing or conflicting data when those risks apply.
Monitoring and independent assurance
Require a plan for monitoring after launch: quality, drift, latency, availability, cost, incidents, user complaints, and override rates. State what constitutes a material model or data change, who approves it, how it is tested, and whether you can reject or roll back it. UK guidance recommends asking about model and training-data origin, limitations, explainability, possible independent audits, and continuing efficacy monitoring.
NIST’s AI Risk Management Framework (AI RMF) 1.0 was released on January 26, 2023. NIST describes it as voluntary and says it is being revised; its Generative AI Profile was released on July 26, 2024. AI RMF can organize risk discussions, but it is not a certification that a supplier or system is safe.
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5. What will the contract actually promise?
A proposal is not a control framework. Convert important answers into enforceable terms with clear owners, evidence, and remedies.
Rank #4
Scope, milestones, and acceptance
- What are the exact deliverables, milestones, dependencies, assumptions, and client responsibilities?
- What objective acceptance criteria apply to each deliverable?
- How are scope changes requested, priced, approved, and documented?
- What happens if a milestone or performance target is missed?
- Which support, maintenance, hosting, usage, or model costs continue after delivery?
Rights, security, and portability
- Who may use your data, prompts, outputs, source code, configurations, documentation, and evaluation results?
- Which intellectual property is pre-existing, and who owns newly developed code, workflows, prompts, and custom models?
- What confidentiality, security-control, incident-notification, audit-access, and subcontractor obligations apply?
- How long are materials retained, and how are they returned or deleted at termination?
- Can you export data, code, configuration, logs, evaluation sets, and documentation in usable formats?
- What assistance and fees apply if you move to another provider?
UK procurement guidance specifically highlights ownership of new intellectual property and ways to limit vendor lock-in. FTC guidance recommends writing security expectations and monitoring into provider contracts. Have counsel align these clauses with privacy, employment, sector, records-retention, and cross-border rules that apply to your organization.
6. How will we operate this after the engagement?
An implementation that only the consulting firm understands creates a long-term dependency. Make operational ownership part of the initial scope.
Knowledge-transfer questions
- What architecture diagrams, runbooks, model cards, data dictionaries, test cases, threat models, and decision logs will we receive?
- Which staff will be trained, on what tasks, and how will competence be demonstrated?
- Can our team deploy, monitor, troubleshoot, update, and, if necessary, disable the system without the firm?
- What support hours, response targets, maintenance tasks, and escalation paths apply?
- Who owns ongoing monitoring, retraining, evaluation, security patching, and model-change approval?
- What is the transition plan if we end the relationship?
If independent operation is not realistic, state the continuing capacity or maintenance contract required, its scope, and its exit conditions. NIST procurement guidance recommends continued provider engagement, training, and lifecycle testing; UK guidance likewise calls for evaluating knowledge transfer and building internal capacity or arranging further maintenance when independent use cannot be assured.
How to compare competing proposals
Give every finalist the same question set and score only evidence that relates to your use case. A useful comparison structure is:
| Evaluation axis | What to examine | Evidence to request |
|---|---|---|
| Problem fit and outcomes | Clarity of the problem, justification for AI, measurable targets, and simpler alternatives | Output-based proposal, baseline, acceptance metrics, and assumptions |
| Delivery capability | Named people, relevant skills, availability, references, and subcontractor responsibility | Team plan, comparable-client contacts, milestone plan, escalation path |
| Data and security | Readiness assumptions, rights, access boundaries, controls, and incident handling | Data map, processing terms, policies, audit evidence, deletion procedure |
| Technical quality and risk | Model and data transparency, limitations, testing, monitoring, auditability, and change control | Architecture, test plan, evaluation results, monitoring design, rollback process |
| Control and continuity | IP and data rights, interoperability, documentation, training, and exit support | Contract language, export specification, runbooks, training plan, transition plan |
| Commercial terms | Scope, milestones, acceptance, dependencies, recurring costs, and remedies | Statement of work, rate and usage assumptions, change process, service terms |
A polished demo does not establish performance on your data or in your operating environment. Match proof to risk: use references for delivery history, written policies for stated practices, contract commitments for obligations, inspected configurations or audit evidence for controls, and a bounded test for performance. Record unresolved assumptions as conditions of award rather than silently accepting them.
A practical sequence before signing
- Write the problem brief. State users, workflow, baseline, desired outcome, constraints, prohibited uses, and the consequences of failure.
- Send identical questions and data requirements. Give each firm the same scenario, evaluation criteria, security expectations, and response deadline.
- Hold a technical and operational workshop. Test assumptions about data access, integrations, human review, deployment, and ownership with the people who will run the system.
- Check evidence. Speak with comparable clients, inspect relevant security material, review the test design, and run a limited proof where risk justifies it.
- Negotiate the statement of work and controls together. Tie payment and acceptance to measurable deliverables; attach data, security, IP, monitoring, and exit terms.
- Approve a staged start. Use discovery and data validation as an explicit gate before committing to full implementation.
- Plan operations before launch. Assign monitoring, incident response, model-change approval, training, maintenance, and termination responsibilities.
Signals to pause or reject a proposal
- The firm starts with a favorite model or demo and cannot define the business outcome.
- It will not name the delivery team, disclose subcontractors, or provide comparable references.
- It assumes clean, complete data without a discovery or validation phase.
- It cannot explain what data leaves your environment, who can access it, or how deletion works.
- It promises accuracy without representative tests, failure thresholds, monitoring, or human escalation.
- It treats a voluntary framework reference as a safety certification.
- It refuses practical export, documentation, training, or transition commitments.
- Important assurances appear only in a presentation and not in the contract.
There is no defensible market-wide figure for typical consulting fees, success rates, or return on investment that can be applied to every engagement. Judge the proposal against your documented baseline, risk tolerance, evidence, and total ongoing cost instead of an unattributed industry statistic.
Relevant official guidance
- UK Government: Guidelines for AI procurement
- NIST: Guidelines for AI Procurement (PDF)
- Federal Trade Commission: Stick with Security
- NIST SP 1326: Cybersecurity Supply Chain Risk Management Due Diligence Assessment Quick-Start Guide
- NIST AI Risk Management Framework
NIST SP 1326 was published on July 8, 2026, as a recent due-diligence reference for ICT suppliers. Its scope includes foreign ownership, control, or influence; provenance; resilience; foundational cyber practices; and supply-chain tiers. Apply it in proportion to your supplier and system risk, and confirm which legal and contractual requirements govern your organization.
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